SearcharxivSearch

arXiv subjects

Harshvardhan Pandey

Publications and source records attributed to Harshvardhan Pandey.

2 recordsLinked to original sources

On the Automorphism Groups of Berman Codes and associated Abelian Codes

The automorphism group of a code is the group of permutations that map a code to itself. Berman codes are a class of binary linear codes characterized by two integer parameters $n\geq 2$ and $m\geq 1$, and this class includes the Reed-Muller codes as well. The class of Berman codes and their duals were recently shown to achieve the capacity of the binary erasure channel. A number of abelian codes that arise from the intersection and subspace sums of Berman and Dual Berman codes were also identified recently, for odd $n\geq 3$. A subclass of these abelian codes was shown to have good short block-length performance for AWGN channels, with efficient decoding algorithms. In this work, we identify the exact automorphism group for Berman codes and their duals. Further, we find the exact automorphism group for the above mentioned abelian codes, when $n\geq 5$. In the case of such abelian codes with $n=3$, we present partial characterizations of the automorphism groups for a large collection of parameter choices, and complete characterizations for a few.

cs.IT

Detecting Convolutional Codes: A Markovian Approach with LRT and DNN

Identifying the unknown convolutional code corresponding to the given intercepted data is an important problem in military surveillance and in wireless communication. While a variety of code identification algorithms are available in the literature, the key contribution of our work lies in the novel solution and the corresponding analysis. In this paper, we focus on the situation when the given data corresponds to either of the two potential convolutional codes and the goal is to detect the correct code. We first provide a new interpretation of the convolutional code as a Markov chain, which is more suitable for analyzing the code detection problem. Our problem then gets reduced to identifying between the two Markov chains. We provide the closed-form expressions for the corresponding state transition matrices and estimate the error exponent for the underlying likelihood ratio test (LRT). We also provide a computationally efficient BCJR-based method for computing the likelihoods required for the LRT. We observe that BCJR-based likelihoods suffer from numerical issues for a longer data sequence, and hence, in this case, we design neural networks that have been found to achieve the optimal performance of the LRT.

cs.IT